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检索条件"任意字段=11th International Conference on Intelligent Data Engineering and Automated Learning"

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Interactive data Visualization Using Dimensionality Reduction and Dissimilarity-Based Representations 18th

Interactive Data Visualization Using Dimensionality Reductio...

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18th international conference on intelligent data engineering and automated learning (IDEAL)

作者： Pena-Unigarro, D. F. Rosero-Montalvo, P. Revelo-Fuelagan, E. J. Castro-Silva, J. A. Alvarado-Perez, J. C. theron, R. Ortega-Bustamante, C. M. Peluffo-Ordonez, D. H. Univ Narino Pasto Colombia Univ Tecn Norte Ibarra Ecuador Inst Tecnol Super 17 Julio Ibarra Ecuador Univ Surcolombiana Neiva Colombia Coorporac Univ Autonoma Narino Pasto Colombia Univ Salamanca Salamanca Spain

ISBN: (纸本)9783319689357;9783319689340

this work describes a new model for interactive data visualization followed from a dimensionality-reduction (DR)-based approach. Particularly, the mixture of the resulting spaces of DR methods is considered, which is carried out by a weighted sum. For the sake of user interaction, corresponding weighting factors are given via an intuitive color-based interface. Also, to depict the DR outcomes while showing information about the input high-dimensional data space, the low-dimensional representations reached by the mixture is conveyed using scatter plots enhanced with an interactive data-driven visualization. In this connection, a constrained dissimilarity approach define the graph to be drawn on the scatter plot.

关键词： data visualization Dimensionality reduction Pairwise dissimilarity

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Semi-supervised Regularized Discriminant Analysis for EEG-Based BCI System 18th

Semi-supervised Regularized Discriminant Analysis for EEG-Ba...

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18th international conference on intelligent data engineering and automated learning (IDEAL)

作者： Xin, Yuhang Wu, Qiang Zhao, Qibin Wu, Qi Shandong Univ Sch Informat Sci & Engn Jinan 250010 Shandong Peoples R China RIKEN Ctr Adv Intelligence Project Tensor Learning Unit Chuo Ku Tokyo 1030027 Japan Shandong Univ Qilu Hosp Jinan 250012 Shandong Peoples R China

ISBN: (纸本)9783319689357;9783319689340

Brain-Computer interface (BCI) is a new technique which allows direction connection between human and computer or other external device. It employs the classification of event-related potential to control the equipment rather than using language or limb movement. Currently, one of the most important issues for ERP-based BCIs is that the ERP classification performance degrades when the training number of samples is small. In order to solve this problem, semi-supervised regularized discriminant analysis (SRDA) was proposed to extract features and classify ERP patterns by integrating semi-supervised learning and regularization approach. the labeled data was used to maximize the separability between different classes and calculate the within-class covariance matrix by regularization. the labeled and unlabeled data were employed to construct the penalty term by neighbor graph. Our proposed approach was evaluated on the BCI Competition Challenge dataset and the simulation results indicated that it achieved a better accuracy than the traditional algorithms.

关键词： Brain computer interface

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Machine learning or discrete choice models for car ownership demand estimation and prediction? 5

Machine learning or discrete choice models for car ownership...

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5th IEEE international conference on Models and Technologies for intelligent Transportation Systems, MT-ITS 2017

作者： Paredes, Miguel Hemberg, Erik O'Reilly, Una-May Zegras, Chris CSAIL and DUSP Massachusetts Institute of Technology CambridgeMA02139 United States

ISBN: (纸本)9781509064847

Discrete choice models are widely used to explain transportation behaviors, including a household's decision to own a car. they show how some distinct choice of human behavior or preference influences a decision. they are also used to project future demand estimates to support policy exploration. this latter use for prediction is indirectly aligned with and conditional to the model's estimation which aims to fit the observed data. In contrast, machine learning models are derived to maximize prediction accuracy through mechanisms such as out-of-sample validation, non-linear structure, and automated covariate selection, albeit at the expense of interpretability and sound behavioral theory. We investigate how machine learning models can outperform discrete choice models for prediction of car ownership using transportation household survey data from Singapore. We compare our household car ownership model (multinomial logit model) against various machine learning models (e.g. Random Forest, Support Vector Machines) by using 2008 data to derive, i.e. estimate models that we then use to predict 2012 ownership. the machine learning models are inferior to the discrete choice model when using discrete choice features. However, after engineering features more appropriate for machine learning they are superior. these results highlight both the cost of applying machine learning models in econometric contexts and an opportunity for improved prediction and better urban policy making through machine learning models with appropriate features. © 2017 IEEE.

关键词： Forecasting

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Perceptions and preferences of high school students in STEM: A case study in Connecticut and Mississippi 11

Perceptions and preferences of high school students in STEM:...

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11th international Multi-conference on Society, Cybernetics and Informatics, IMSCI 2017

作者： Zhou, Bin Anderson, Clifford Wang, Feng Li, Lin Department of Engineering Central Connecticut State University New BritainCT06050 United States Department of Civil and Environmental Engineering Jackson State University JacksonMI39217 United States

ISBN: (纸本)9781941763650

this study uses data collected at two National Summer Transportation Institute (NSTI) programs in Connecticut and Mississippi to investigate high school students' perceptions and preferences about education in science, technology, engineering and mathematics (STEM). Family background has a significant impact on a high school student's interest in STEM, as shown during the student recruitment stage and by the analysis of the students' college education plans prepared upon graduation from the two NSTI programs. the building exercise and competition instrument is the most effective among the few examined, while passive learning is not what young people prefer when briefly introduced in the two NSTI programs.

关键词： Students

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WENC: HTTPS Encrypted Traffic Classification Using Weighted Ensemble learning and Markov Chain 16

WENC: HTTPS Encrypted Traffic Classification Using Weighted ...

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16th IEEE international conference on Trust, Security and Privacy in Computing and Communications / the 11th IEEE international conference on Big data Science and engineering / the 14th IEEE international conference on Embedded Software and Systems

作者： Pan, Wubin Cheng, Gaung Tang, Yongning Southeast Univ Sch Comp Sci & Engn Nanjing Jiangsu Peoples R China Minist Educ Key Lab Comp Network & Informat Integrat Beijing Peoples R China Illinois State Univ Sch Informat Technol Normal IL 61761 USA

ISBN: (纸本)9781509049066

SSL/TLS protocol is widely used for secure web applications (i.e., HTTPS). Classifying encrypted SSL/TLS based applications is an important but challenging task for network management. Traditional traffic classification methods are incapable of accomplishing this task. Several recently proposed approaches that focused on discriminating defining fingerprints among various SSL/TLS applications have also shown various limitations. In this paper, we design a Weighted ENsemble Classifier (WENC) to tackle these limitations. WENC studies the characteristics of various sub-flows during the HTTPS handshake process and the following data transmission period. To increase the fingerprint recognizability, we propose to establish a second order Markov chain model with a fingerprint variable jointly considering the packet length and the message type during the process of HTTPS handshake. Furthermore, the series of the packet lengths of application data is modeled as HMM with optimal emission probability. Finally, a weighted ensemble strategy is devised to accommodate the advantages of several approaches as a unified one. Experimental results show that the classification accuracy of the proposed method reaches 90%, with an 11% improvement on average comparing to the state-of-the-art methods.

关键词： SSL/TLS HTTPS services HMM Markov chain, weighted ensemble

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Proceedings - 2017 5th international conference on Applied Computing and Information Technology, 2017 4th international conference on Computational Science/Intelligence and Applied Informatics and 2017 1st international conference on Big data, Cloud Computing, data Science and engineering, ACIT-CSII-BCD 2017

Proceedings - 2017 5th International Conference on Applied C...

引用

5th international conference on Applied Computing and Information Technology, 2017 4th international conference on Computational Science/Intelligence and Applied Informatics and 2017 1st international conference on Big data, Cloud Computing, data Science and engineering, ACIT-CSII-BCD 2017

ISBN: (纸本)9781538633021

the proceedings contain 44 papers. the topics discussed include: a learning material for physics experiment with high-accuracy using computer vision technique;automatic classification of remarks in werewolf BBS;a rapid incremental frequent pattern mining algorithm for uncertain data;global and local bursts detection in streaming data;two-mode three-way dominance points model for periodic dissimilarity;an intelligent noninvasive taste detection app for watermelons;automated risk identification of CMMI project planning using ontology;and depth recognition in 3D translucent stereoscopic imaging of medical volumes by means of a glasses-free 3d display.

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Optimal design of reversible parity preserving new Full adder / Full subtractor 11

Optimal design of reversible parity preserving new Full adde...

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2017 11th international conference on intelligent Systems and Control, ISCO 2017

作者： Kumar, P.Kiran Rao, P.Prasad Kakarla, Hari Kishore Dept. of ECE KL University India Dept. of ECE Vaagdevi Engineering College India

ISBN: (纸本)9781509027170

the widely using CMOS technology implementing with irreversible logic will hit a scaling limit beyond 2020 and the major limiting factor is increased power dissipation. the irreversible logic is replaced by reversible logic to decrease the power dissipation. the devices implemented with reversible logic gates will have demand for the upcoming future computing technologies as they consumes less power. Reversible logic has applications in Low Power VLSI, Quantum Computing, Nanotechnology and Optical computing. this paper proposes the design of a optimal fault tolerant Full adder / Full subtractor. For this logic circuit input parity and output parity is same hence it is called parity preserving circuit. the proposed method require less complexity, less hardware, minimum number of gates, minimum number of garbage inputs and minimum number of constant inputs than existing methods. © 2017 IEEE.

关键词： Optical data processing

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ASID: Advanced System for Process Control towards intelligent Specialization in the Power engineering Field 9

ASID: Advanced System for Process Control towards Intelligen...

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9th IEEE international conference on intelligent data Acquisition and Advanced Computing Systems - Technology and Applications (IDAACS)

作者： Stamatescu, Iulia Stamatescu, Grigore Fagarasan, Ioana Arghira, Nicoleta Calofir, Vasile Iliescu, Sergiu Stelian Univ Politehn Bucuresti Dept Automat Control & Ind Informat 313 Splaiul Independentei Bucharest 060042 Romania

ISBN: (纸本)9781538606971

the paper presents the system architecture, development and prototype implementation of a new integrated system for simulation of automated industrial processes using advanced technologies, in accordance with CPS/Industry 4.0 principles. the need to develop such a system is underscored by the interest of the educational stakeholders: students, faculty members, high-level industry partners, for an educational system to be used in specialized training in interdisciplinary areas including edge systems in terms of modern control technologies, open systems available to be reconfigured upon request with other technologies and mission-critical applications for different process engineering fields such as key energy applications. the process simulator can be operated in educational environments as follows: as didactic equipment for learning and deepening PLCs programming languages skills;equipment for testing various complex scenarios of some energy processes while allowing on-demand reconfiguration for some industrial systems which cannot be implemented in reality due to the high cost and/or the operational safety. Given its properties flexibility, interoperability, open architecture, compatibility in communication, friendly human-machine interface and industrial applications, ASID is able to cover a vast array of teaching scenarios as well as serve as a platform for control algorithm implementation and testing. the modular configuration allows for increased versatility and flexibility and the long-term competitiveness of the final demonstrator is assured through hardware and software updates.

关键词： process control industry automation simulation embedded systems cyber-physical systems

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learning Convolutional Ranking-Score Function by Query Preference Regularization 1

引用

18th international conference on intelligent data engineering and automated learning (IDEAL)

作者： Zhang, Guohui Liang, Gaoyuan Li, Weizhi Fang, Jian Wang, Jingbin Geng, Yanyan Wang, Jing-Yan Huawei Technol Co Ltd Shanghai Peoples R China Jiangsu Univ Technol Nanjing 213001 Jiangsu Peoples R China Reflektion Inc San Mateo CA 94404 USA Auburn Univ Auburn AL 36849 USA Intermediate Peoples Court Linyi City Informat Technol Serv Ctr Linyi Peoples R China Soochow Univ Prov Key Lab Comp Informat Proc Technol Suzhou 215006 Peoples R China Nanjing Univ Informat Sci & Technol Collaborat Innovat Ctr Atmospher Environm & Equip Jiangsu Key Lab Big Data Anal Technol B DAT Nanjing Jiangsu Peoples R China

ISBN: (数字)9783319689357

ISBN: (纸本)9783319689357;9783319689340

Ranking score plays an important role in the system of content-based retrieval. Given a query, the database items are ranked according to the ranking scores in a descending order, and the top-ranked items are returned as retrieval results. In this paper, we propose a new ranking scoring function based on the convolutional neural network (CNN). the ranking scoring function has a structure of CNN, and its parameters are adjusted to both queries and query preferences. the learning process guarantees that the ranking score of the query itself is large, and also the ranking scores of the positives (database items which the query wants to link) are larger than those of the negatives (database items which the query wants to avoid). Moreover, we also impose that the neighboring database items have similar ranking scores. An optimization problem is formulated and solved by Estimation-Maximization method. Experiments over the benchmark data sets show the advantage over the existing learning-to-rank methods.

关键词： Convolutional neural network Content-based retrieval Ranking score Estimation-maximization method

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Deep learning in medical image analysis: Recent advances and future trends 11

Deep learning in medical image analysis: Recent advances and...

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11th international conferences on Computer Graphics, Visualization, Computer Vision and Image Processing, CGVCVIP 2017 and international conference on Big data Analytics, data Mining and Computational Intelligence, BigDaCI 2017

作者： Goceri, Evgin Goceri, Numan Akdeniz University Engineering Faculty Computer Engineering Department Dumlupinar Boulevard Antalya07058 Turkey Evosoft GmbH Business Function Information Technology Solutions Marienbergstr. 78-80 Nuremberg90411 Germany

ISBN: (纸本)9789898533661

Deep learning (DL) methods are a set of algorithms in Machine learning (ML), which provides an effective way to analysis medical images automatically for diagnosis/assessment of a disease. DL enables higher level of abstraction and provides better prediction from datasets. therefore, DL has a great impact and become popular in recent years. In this work, we present advances and future researches on DL based medical image analysis. © 2017.

关键词： Image analysis

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